PSICQUIC

Description

PSICQUIC is an effort from the HUPO Proteomics Standard Initiative (HUPO-PSI) to standardise programmatic access to molecular interaction databases. The Bioconductor PSICQUIC package provides a traditional R interface layered on top of the PSICQUIC REST interface. Gene symbols are most commonly used in queries; interactions are returned in a data.frame, characterized by interaction type, detection method, and publication references. Confidence scores are sometimes avaialable. Queries may be constrained by many of these same attributes, i.e., interaction type, detection method, species, publication identifier, and source database.

Details

There are two operational differences between the native PSIQUIC REST interface and that offered here via the interactions method:

species exclusivity:

The REST interface requires only that one participant in an interaction be from the named species. By default, we require that both participants are from the named species. This can be controlled by the speciesExclusive logical argument to the interactions method.

number of molecules (or identifiers):

the REST interface permits only zero, one or two gene or protein identifiers per query. We allow any number, zero or more and, when the number is greater than or equal to two, the interactions returned are only those in which any two of those identifiers participate. If you want all interactions which include any of a list of identifiers, and you don't care to control for their partners, you can accomplish this by issuing successive single-identifer interaction queries.

Constructor

PSICQUIC: contacts the central PSICQUIC web server, discovers currently functioning servers, returns an object used in the methods below.

Methods

providers(x): lists the short names of the data providers

interactions(x,id, species, speciesExclusive, type, provider,detectionMethod, publicationID, quiet): retrieves all interactions matching the specified pattern.

rawQuery(x, provider, rawArgs): query terms in native PSICQUIC REST style

show(x): displays current providers and related data

Functions

detectionMethods(): your web browser will display the PSI-MI ontology for detection methods

interactionTypes(): your web browser will display the PSI-MI ontology for molecular interaction types

Author(s)

Paul Shannon

See Also

providers, interactions, rawQuery, addGeneInfo, IDMapper, interactionTypes, detectionMethods, speciesIDs

Examples

   psicquic <- PSICQUIC()
     # obtain the list of two dozen (or so) currently live
     # PSICQUIC-compliant data providers
   providers(psicquic)
     # a minimal call: get all interactions with MAP3K3, of all types,
     # from all providers.  a data.frame is returned
   tbl.0 <- interactions(psicquic, "MAP3K3", species="9606")

     # build a contingency table, sort it, and see
     # what kinds of interactions were returned, obtained
     # by what detection methods.
     # "-" is used when the provider does not specify a value.
     # you will see a wide range of specificity, in detection method,
     # interaction type, and number of interactions found.

   xtab <- with(tbl.0, as.data.frame(
                table(type, detectionMethod,  provider)))
   xtab <- subset(xtab, Freq > 0) # [order(xtab$Freq, decreasing=TRUE),]
   xtab <- xtab[order(xtab$Freq, decreasing=TRUE),]

     # what interactors were returned?  the IDMapper class in this
     # package converts many PSICQUIC providers' protein identifiers to
     # entrez geneIDs and HUGO gene symbols, via remote calls to
     # biomaRt:

   idMapper <- IDMapper("9606")
   tbl.0g <- addGeneInfo(idMapper, tbl.0)
   with(tbl.0g, head(unique(c(A.name, B.name))))

      # we see that MAP2K5 is the most frequently mentioned interacator:

   xtab.sym <- with(tbl.0g, table(c(A.name, B.name)))
   head(sort(xtab.sym, decreasing=TRUE))

      # PSIQUIC uses well-devloped ontologies -- controlled vocabularies --
      # which are currently best viewed in a web browser.
      # we provide two convenience functions which will display these
      # hierarchically defined vocabularies:

   # interactionTypes()
   # detectionMethods()

      # NCBI curates taxonomy codes, such as "9606" for Homo sapiens.
      # you can find these codes by using this method, which will
      # drive your browser to the appropriate NCBI web page.

   # speciesIds()

      # use terms from these vocabularies to retrieve interaction
      # information for these two proteins.  note that both of
      # these terms are mid-level in their respective hierarchies
      # and will likely retrieve more specific nested terms

   tbl.2 <- interactions(psicquic, id=c("MAP3K3", "MAP2K5"),
                         species="9606",
                         type="physical association",
                         detectionMethod="affinity chromatography technology")

      # add gene IDs and symbols

   tbl.2g <- addGeneInfo(idMapper, tbl.2)

      # how many publications lie behind these interactions?
   tbl.2g[, c("A.name", "B.name", "detectionMethod", "firstAuthor")]

      # the package also provides a convenience method for submitting
      # queries in native MIQL (Molecular Interaction Query Language).
      # the language is defined here:
      # http://code.google.com/p/psicquic/wiki/MiqlReference27

   if("BioGrid" %in% providers(psicquic)){
      tbl.3 <- rawQuery(psicquic, "BioGrid", "identifier:ALK AND species:9606")
         # what publications?
      table(tbl.3$V8)
      }

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(PSICQUIC)
Loading required package: IRanges
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: biomaRt
Loading required package: httr
Loading required package: plyr

Attaching package: 'plyr'

The following object is masked from 'package:IRanges':

    desc

The following object is masked from 'package:S4Vectors':

    rename

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/PSICQUIC/PSICQUIC-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: PSICQUIC-class
> ### Title: PSICQUIC
> ### Aliases: class:PSICQUIC PSICQUIC-class PSICQUIC show,PSICQUIC-method
> ### Keywords: methods classes
> 
> ### ** Examples
> 
>    psicquic <- PSICQUIC()
>      # obtain the list of two dozen (or so) currently live
>      # PSICQUIC-compliant data providers
>    providers(psicquic)
 [1] "APID"              "BioGrid"           "bhf-ucl"          
 [4] "ChEMBL"            "DIP"               "HPIDb"            
 [7] "InnateDB"          "InnateDB-All"      "IntAct"           
[10] "mentha"            "MPIDB"             "MatrixDB"         
[13] "MINT"              "Reactome"          "Reactome-FIs"     
[16] "I2D"               "I2D-IMEx"          "InnateDB-IMEx"    
[19] "MolCon"            "UniProt"           "MBInfo"           
[22] "BindingDB"         "VirHostNet"        "Spike"            
[25] "BAR"               "EBI-GOA-nonIntAct" "ZINC"             
>      # a minimal call: get all interactions with MAP3K3, of all types,
>      # from all providers.  a data.frame is returned
>    tbl.0 <- interactions(psicquic, "MAP3K3", species="9606")
> 
>      # build a contingency table, sort it, and see
>      # what kinds of interactions were returned, obtained
>      # by what detection methods.
>      # "-" is used when the provider does not specify a value.
>      # you will see a wide range of specificity, in detection method,
>      # interaction type, and number of interactions found.
> 
>    xtab <- with(tbl.0, as.data.frame(
+                 table(type, detectionMethod,  provider)))
>    xtab <- subset(xtab, Freq > 0) # [order(xtab$Freq, decreasing=TRUE),]
>    xtab <- xtab[order(xtab$Freq, decreasing=TRUE),]
> 
>      # what interactors were returned?  the IDMapper class in this
>      # package converts many PSICQUIC providers' protein identifiers to
>      # entrez geneIDs and HUGO gene symbols, via remote calls to
>      # biomaRt:
> 
>    idMapper <- IDMapper("9606")
checking for biomart access...
   does 'http://www.ensembl.org' respond?
   creating ensembl mart
   hsapiens_gene_ensembl dataset provided?
connecting to biomart...
>    tbl.0g <- addGeneInfo(idMapper, tbl.0)
>    with(tbl.0g, head(unique(c(A.name, B.name))))
[1] "MAP3K3" "-"      "BRCA1"  "MAP2K5" "WNK1"   "CHUK"  
> 
>       # we see that MAP2K5 is the most frequently mentioned interacator:
> 
>    xtab.sym <- with(tbl.0g, table(c(A.name, B.name)))
>    head(sort(xtab.sym, decreasing=TRUE))

MAP3K3      - MAP2K5  TRAF6  TRAF7  YWHAE 
   797    300     30     16     16     16 
> 
>       # PSIQUIC uses well-devloped ontologies -- controlled vocabularies --
>       # which are currently best viewed in a web browser.
>       # we provide two convenience functions which will display these
>       # hierarchically defined vocabularies:
> 
>    # interactionTypes()
>    # detectionMethods()
> 
>       # NCBI curates taxonomy codes, such as "9606" for Homo sapiens.
>       # you can find these codes by using this method, which will
>       # drive your browser to the appropriate NCBI web page.
> 
>    # speciesIds()
> 
>       # use terms from these vocabularies to retrieve interaction
>       # information for these two proteins.  note that both of
>       # these terms are mid-level in their respective hierarchies
>       # and will likely retrieve more specific nested terms
> 
>    tbl.2 <- interactions(psicquic, id=c("MAP3K3", "MAP2K5"),
+                          species="9606",
+                          type="physical association",
+                          detectionMethod="affinity chromatography technology")
> 
>       # add gene IDs and symbols
> 
>    tbl.2g <- addGeneInfo(idMapper, tbl.2)
> 
>       # how many publications lie behind these interactions?
>    tbl.2g[, c("A.name", "B.name", "detectionMethod", "firstAuthor")]
   A.name B.name                                    detectionMethod
1  MAP2K5 MAP3K3 psi-mi:MI:0004(affinity chromatography technology)
2  MAP3K3 MAP2K5 psi-mi:MI:0004(affinity chromatography technology)
3  MAP3K3 MAP2K5 psi-mi:MI:0004(affinity chromatography technology)
4  MAP3K3 MAP2K5 psi-mi:MI:0004(affinity chromatography technology)
5       -      - psi-mi:MI:0004(affinity chromatography technology)
6       -      - psi-mi:MI:0004(affinity chromatography technology)
7       -      - psi-mi:MI:0004(affinity chromatography technology)
8       -      - psi-mi:MI:0004(affinity chromatography technology)
9  MAP3K3 MAP2K5       psi-mi:MI:0676(tandem affinity purification)
10 MAP2K5 MAP3K3           psi-mi:MI:0813(proximity ligation assay)
11 MAP3K3 MAP2K5 psi-mi:MI:0004(affinity chromatography technology)
12 MAP3K3 MAP2K5 psi-mi:MI:0004(affinity chromatography technology)
13 MAP3K3 MAP2K5 psi-mi:MI:0004(affinity chromatography technology)
14 MAP3K3 MAP2K5 psi-mi:MI:0004(affinity chromatography technology)
                 firstAuthor
1               Sun W (2001)
2       Bouwmeester T (2004)
3          Nakamura K (2010)
4            Lamark T (2003)
5      Nakamura et al.(2010)
6       Lamark et al. (2003)
7          Sun et al. (2001)
8  Bouwmeester et al. (2004)
9  Bouwmeester et al. (2004)
10        Chen et al. (2014)
11                         -
12                         -
13                         -
14                         -
> 
>       # the package also provides a convenience method for submitting
>       # queries in native MIQL (Molecular Interaction Query Language).
>       # the language is defined here:
>       # http://code.google.com/p/psicquic/wiki/MiqlReference27
> 
>    if("BioGrid" %in% providers(psicquic)){
+       tbl.3 <- rawQuery(psicquic, "BioGrid", "identifier:ALK AND species:9606")
+          # what publications?
+       table(tbl.3$V8)
+       }

 Ambrogio C (2005)      Bai RY (1998)   Bonvini P (2002) Crockett DK (2004) 
                20                  3                  2                 43 
  Fenner BJ (2010)    Miyake I (2002)    Ouyang T (2003)  Pao-Chun L (2009) 
                 1                  1                  3                  2 
     Ren SY (2005)   Stoica GE (2001)   Taipale M (2012)      Zamo A (2002) 
                 1                  1                  2                  1 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>